UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents

• URL: http://arxiv.org/abs/2604.11557v1
• Date: Mon, 13 Apr 2026 14:43:47 GMT
• Title: UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents
• Abstract summary: We present UniToolCall, a unified framework for tool learning that standardizes the entire pipeline from construction and dataset generation to evaluation.<n>We convert 7 public benchmarks into a unified Query-Action--Observation-Answer (QAOA) representation with fine-grained evaluation at the function-call, turn, and conversation levels.<n>Under the Anchoror-heavy Hybrid-20 setting, achieves 93.0% single-turn Strict Precision, outperforming commercial models including GPT, Gemini, and Claude.
• Score: 22.52508596251479
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tool-use capability is a fundamental component of LLM agents, enabling them to interact with external systems through structured function calls. However, existing research exhibits inconsistent interaction representations, largely overlooks the structural distribution of tool-use trajectories, and relies on incompatible evaluation benchmarks. We present UniToolCall, a unified framework for tool learning that standardizes the entire pipeline from toolset construction and dataset generation to evaluation. The framework curates a large tool pool of 22k+ tools and constructs a hybrid training corpus of 390k+ instances by combining 10 standardized public datasets with structurally controlled synthetic trajectories. It explicitly models diverse interaction patterns, including single-hop vs. multi-hop and single-turn vs. multi-turn, while capturing both serial and parallel execution structures. To support coherent multi-turn reasoning, we further introduce an Anchor Linkage mechanism that enforces cross-turn dependencies. Furthermore, we convert 7 public benchmarks into a unified Query--Action--Observation--Answer (QAOA) representation with fine-grained evaluation at the function-call, turn, and conversation levels. Experiments show that fine-tuning Qwen3-8B on our dataset substantially improves tool-use performance. Under the distractor-heavy Hybrid-20 setting, achieves 93.0% single-turn Strict Precision, outperforming commercial models including GPT, Gemini, and Claude.

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